Abstract Theory of mind (ToM) broadly refers to humans’ ability to represent the mental
states of others, including their desires, beliefs, and intentions. We design a Theory of Mind …

The auditory system must represent sounds with a wide range of statistical properties. One
important property is the spectrotemporal contrast in the acoustic environment: the variation in …

The ability to learn tasks in a sequential fashion is crucial to the development of artificial
intelligence. Until now neural networks have not been capable of this and it has been widely …

Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding
catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The …

Reinforcement learning (RL) has shown great success in increasingly complex single-agent
environments and two-player turn-based games. However, the real world contains multiple …

Deep neural networks have achieved impressive successes in fields ranging from object
recognition to complex games such as Go 1 , 2 . Navigation, however, remains a substantial …

Scene representation—the process of converting visual sensory data into concise descriptions—is
a requirement for intelligent behavior. Recent work has shown that neural networks …

Despite their ability to memorize large datasets, deep neural networks often achieve good
generalization performance. However, the differences between the learned solutions of …

One of the key challenges of artificial intelligence is to learn models that are effective in the
context of planning. In this document we introduce the predictron architecture. The predictron …

Effective exploration is a challenge in reinforcement learning (RL). Novelty-based exploration
methods can suffer in high-dimensional state spaces, such as continuous partially-…